Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations700
Missing cells53
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory87.6 KiB
Average record size in memory128.2 B

Variable types

Categorical6
Numeric9
DateTime1

Alerts

Sales is highly overall correlated with COGS and 4 other fieldsHigh correlation
COGS is highly overall correlated with Sales and 3 other fieldsHigh correlation
Discounts is highly overall correlated with Sales and 3 other fieldsHigh correlation
Gross Sales is highly overall correlated with Sales and 4 other fieldsHigh correlation
Manufacturing Price is highly overall correlated with ProductHigh correlation
Month Name is highly overall correlated with Month Number and 1 other fieldsHigh correlation
Month Number is highly overall correlated with Month Name and 1 other fieldsHigh correlation
Product is highly overall correlated with Manufacturing PriceHigh correlation
Profit is highly overall correlated with Sales and 2 other fieldsHigh correlation
Sale Price is highly overall correlated with Sales and 5 other fieldsHigh correlation
Segment is highly overall correlated with Sale PriceHigh correlation
Year is highly overall correlated with Month Name and 1 other fieldsHigh correlation
Discount Band has 53 (7.6%) missing values Missing
Country is uniformly distributed Uniform
Discounts has 53 (7.6%) zeros Zeros

Reproduction

Analysis started2024-11-19 13:05:21.825621
Analysis finished2024-11-19 13:05:45.313398
Duration23.49 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Segment
Categorical

High correlation 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Government
300 
Midmarket
100 
Channel Partners
100 
Enterprise
100 
Small Business
100 

Length

Max length16
Median length10
Mean length11.285714
Min length9

Characters and Unicode

Total characters7900
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGovernment
2nd rowGovernment
3rd rowMidmarket
4th rowMidmarket
5th rowMidmarket

Common Values

ValueCountFrequency (%)
Government 300
42.9%
Midmarket 100
 
14.3%
Channel Partners 100
 
14.3%
Enterprise 100
 
14.3%
Small Business 100
 
14.3%

Length

2024-11-19T13:05:45.507293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T13:05:45.811605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
government 300
33.3%
midmarket 100
 
11.1%
channel 100
 
11.1%
partners 100
 
11.1%
enterprise 100
 
11.1%
small 100
 
11.1%
business 100
 
11.1%

Most occurring characters

ValueCountFrequency (%)
e 1200
15.2%
n 1100
13.9%
r 800
10.1%
t 600
 
7.6%
m 500
 
6.3%
s 500
 
6.3%
a 400
 
5.1%
G 300
 
3.8%
o 300
 
3.8%
v 300
 
3.8%
Other values (14) 1900
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1200
15.2%
n 1100
13.9%
r 800
10.1%
t 600
 
7.6%
m 500
 
6.3%
s 500
 
6.3%
a 400
 
5.1%
G 300
 
3.8%
o 300
 
3.8%
v 300
 
3.8%
Other values (14) 1900
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1200
15.2%
n 1100
13.9%
r 800
10.1%
t 600
 
7.6%
m 500
 
6.3%
s 500
 
6.3%
a 400
 
5.1%
G 300
 
3.8%
o 300
 
3.8%
v 300
 
3.8%
Other values (14) 1900
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1200
15.2%
n 1100
13.9%
r 800
10.1%
t 600
 
7.6%
m 500
 
6.3%
s 500
 
6.3%
a 400
 
5.1%
G 300
 
3.8%
o 300
 
3.8%
v 300
 
3.8%
Other values (14) 1900
24.1%

Country
Categorical

Uniform 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Canada
140 
Germany
140 
France
140 
Mexico
140 
United States of America
140 

Length

Max length24
Median length6
Mean length9.8
Min length6

Characters and Unicode

Total characters6860
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanada
2nd rowGermany
3rd rowFrance
4th rowGermany
5th rowMexico

Common Values

ValueCountFrequency (%)
Canada 140
20.0%
Germany 140
20.0%
France 140
20.0%
Mexico 140
20.0%
United States of America 140
20.0%

Length

2024-11-19T13:05:46.148502image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T13:05:46.401593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
canada 140
12.5%
germany 140
12.5%
france 140
12.5%
mexico 140
12.5%
united 140
12.5%
states 140
12.5%
of 140
12.5%
america 140
12.5%

Most occurring characters

ValueCountFrequency (%)
a 980
14.3%
e 840
12.2%
n 560
 
8.2%
t 420
 
6.1%
r 420
 
6.1%
c 420
 
6.1%
420
 
6.1%
i 420
 
6.1%
d 280
 
4.1%
m 280
 
4.1%
Other values (12) 1820
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 980
14.3%
e 840
12.2%
n 560
 
8.2%
t 420
 
6.1%
r 420
 
6.1%
c 420
 
6.1%
420
 
6.1%
i 420
 
6.1%
d 280
 
4.1%
m 280
 
4.1%
Other values (12) 1820
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 980
14.3%
e 840
12.2%
n 560
 
8.2%
t 420
 
6.1%
r 420
 
6.1%
c 420
 
6.1%
420
 
6.1%
i 420
 
6.1%
d 280
 
4.1%
m 280
 
4.1%
Other values (12) 1820
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 980
14.3%
e 840
12.2%
n 560
 
8.2%
t 420
 
6.1%
r 420
 
6.1%
c 420
 
6.1%
420
 
6.1%
i 420
 
6.1%
d 280
 
4.1%
m 280
 
4.1%
Other values (12) 1820
26.5%

Product
Categorical

High correlation 

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Paseo
202 
Velo
109 
VTT
109 
Amarilla
94 
Carretera
93 

Length

Max length9
Median length7
Mean length5.7328571
Min length3

Characters and Unicode

Total characters4013
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCarretera
2nd rowCarretera
3rd rowCarretera
4th rowCarretera
5th rowCarretera

Common Values

ValueCountFrequency (%)
Paseo 202
28.9%
Velo 109
15.6%
VTT 109
15.6%
Amarilla 94
13.4%
Carretera 93
13.3%
Montana 93
13.3%

Length

2024-11-19T13:05:46.682652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T13:05:46.945566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
paseo 202
28.9%
velo 109
15.6%
vtt 109
15.6%
amarilla 94
13.4%
carretera 93
13.3%
montana 93
13.3%

Most occurring characters

ValueCountFrequency (%)
a 762
19.0%
e 497
12.4%
o 404
10.1%
r 373
9.3%
l 297
 
7.4%
V 218
 
5.4%
T 218
 
5.4%
P 202
 
5.0%
s 202
 
5.0%
t 186
 
4.6%
Other values (6) 654
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4013
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 762
19.0%
e 497
12.4%
o 404
10.1%
r 373
9.3%
l 297
 
7.4%
V 218
 
5.4%
T 218
 
5.4%
P 202
 
5.0%
s 202
 
5.0%
t 186
 
4.6%
Other values (6) 654
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4013
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 762
19.0%
e 497
12.4%
o 404
10.1%
r 373
9.3%
l 297
 
7.4%
V 218
 
5.4%
T 218
 
5.4%
P 202
 
5.0%
s 202
 
5.0%
t 186
 
4.6%
Other values (6) 654
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4013
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 762
19.0%
e 497
12.4%
o 404
10.1%
r 373
9.3%
l 297
 
7.4%
V 218
 
5.4%
T 218
 
5.4%
P 202
 
5.0%
s 202
 
5.0%
t 186
 
4.6%
Other values (6) 654
16.3%

Discount Band
Categorical

Missing 

Distinct3
Distinct (%)0.5%
Missing53
Missing (%)7.6%
Memory size5.6 KiB
High
245 
Medium
242 
Low
160 

Length

Max length6
Median length4
Mean length4.5007728
Min length3

Characters and Unicode

Total characters2912
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowLow
4th rowLow
5th rowLow

Common Values

ValueCountFrequency (%)
High 245
35.0%
Medium 242
34.6%
Low 160
22.9%
(Missing) 53
 
7.6%

Length

2024-11-19T13:05:47.263463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T13:05:47.551408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
high 245
37.9%
medium 242
37.4%
low 160
24.7%

Most occurring characters

ValueCountFrequency (%)
i 487
16.7%
H 245
8.4%
g 245
8.4%
h 245
8.4%
M 242
8.3%
e 242
8.3%
d 242
8.3%
u 242
8.3%
m 242
8.3%
L 160
 
5.5%
Other values (2) 320
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 487
16.7%
H 245
8.4%
g 245
8.4%
h 245
8.4%
M 242
8.3%
e 242
8.3%
d 242
8.3%
u 242
8.3%
m 242
8.3%
L 160
 
5.5%
Other values (2) 320
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 487
16.7%
H 245
8.4%
g 245
8.4%
h 245
8.4%
M 242
8.3%
e 242
8.3%
d 242
8.3%
u 242
8.3%
m 242
8.3%
L 160
 
5.5%
Other values (2) 320
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 487
16.7%
H 245
8.4%
g 245
8.4%
h 245
8.4%
M 242
8.3%
e 242
8.3%
d 242
8.3%
u 242
8.3%
m 242
8.3%
L 160
 
5.5%
Other values (2) 320
11.0%

Units Sold
Real number (ℝ)

Distinct510
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.2943
Minimum200
Maximum4492.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-19T13:05:47.824597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile344.95
Q1905
median1542.5
Q32229.125
95-th percentile2935.95
Maximum4492.5
Range4292.5
Interquartile range (IQR)1324.125

Descriptive statistics

Standard deviation867.42786
Coefficient of variation (CV)0.53934648
Kurtosis-0.315318
Mean1608.2943
Median Absolute Deviation (MAD)655.5
Skewness0.43615356
Sum1125806
Variance752431.09
MonotonicityNot monotonic
2024-11-19T13:05:48.200102image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
727 5
 
0.7%
2844 4
 
0.6%
1916 4
 
0.6%
663 4
 
0.6%
1743 4
 
0.6%
1496 4
 
0.6%
1372 3
 
0.4%
1366 3
 
0.4%
293 3
 
0.4%
1123 3
 
0.4%
Other values (500) 663
94.7%
ValueCountFrequency (%)
200 1
0.1%
214 2
0.3%
218 1
0.1%
241 2
0.3%
245 1
0.1%
257 1
0.1%
259 1
0.1%
260 1
0.1%
263 2
0.3%
266 1
0.1%
ValueCountFrequency (%)
4492.5 1
0.1%
4251 1
0.1%
4243.5 1
0.1%
4219.5 1
0.1%
4026 1
0.1%
3997.5 1
0.1%
3945 1
0.1%
3874.5 1
0.1%
3864 1
0.1%
3850.5 1
0.1%

Manufacturing Price
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.477143
Minimum3
Maximum260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-19T13:05:48.776910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median10
Q3250
95-th percentile260
Maximum260
Range257
Interquartile range (IQR)245

Descriptive statistics

Standard deviation108.60261
Coefficient of variation (CV)1.1256823
Kurtosis-1.4289627
Mean96.477143
Median Absolute Deviation (MAD)7
Skewness0.59258395
Sum67534
Variance11794.527
MonotonicityNot monotonic
2024-11-19T13:05:49.003834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10 202
28.9%
120 109
15.6%
250 109
15.6%
260 94
13.4%
3 93
13.3%
5 93
13.3%
ValueCountFrequency (%)
3 93
13.3%
5 93
13.3%
10 202
28.9%
120 109
15.6%
250 109
15.6%
260 94
13.4%
ValueCountFrequency (%)
260 94
13.4%
250 109
15.6%
120 109
15.6%
10 202
28.9%
5 93
13.3%
3 93
13.3%

Sale Price
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.42857
Minimum7
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-19T13:05:49.229705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q112
median20
Q3300
95-th percentile350
Maximum350
Range343
Interquartile range (IQR)288

Descriptive statistics

Standard deviation136.77551
Coefficient of variation (CV)1.1549199
Kurtosis-1.176789
Mean118.42857
Median Absolute Deviation (MAD)13
Skewness0.77128187
Sum82900
Variance18707.541
MonotonicityNot monotonic
2024-11-19T13:05:49.450065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
20 100
14.3%
15 100
14.3%
350 100
14.3%
12 100
14.3%
125 100
14.3%
300 100
14.3%
7 100
14.3%
ValueCountFrequency (%)
7 100
14.3%
12 100
14.3%
15 100
14.3%
20 100
14.3%
125 100
14.3%
300 100
14.3%
350 100
14.3%
ValueCountFrequency (%)
350 100
14.3%
300 100
14.3%
125 100
14.3%
20 100
14.3%
15 100
14.3%
12 100
14.3%
7 100
14.3%

Gross Sales
Real number (ℝ)

High correlation 

Distinct550
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182759.43
Minimum1799
Maximum1207500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-19T13:05:49.731884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1799
5-th percentile5193.05
Q117391.75
median37980
Q3279025
95-th percentile754250
Maximum1207500
Range1205701
Interquartile range (IQR)261633.25

Descriptive statistics

Standard deviation254262.28
Coefficient of variation (CV)1.3912403
Kurtosis2.0543006
Mean182759.43
Median Absolute Deviation (MAD)30747.5
Skewness1.6739217
Sum1.279316 × 108
Variance6.4649309 × 1010
MonotonicityNot monotonic
2024-11-19T13:05:50.067966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37050 3
 
0.4%
738000 3
 
0.4%
22710 3
 
0.4%
4404 3
 
0.4%
82875 3
 
0.4%
26145 3
 
0.4%
349625 2
 
0.3%
24980 2
 
0.3%
921200 2
 
0.3%
196250 2
 
0.3%
Other values (540) 674
96.3%
ValueCountFrequency (%)
1799 1
0.1%
1841 2
0.3%
1960 2
0.3%
2051 1
0.1%
2520 2
0.3%
2534 1
0.1%
2660 1
0.1%
2716 1
0.1%
3270 1
0.1%
3416 1
0.1%
ValueCountFrequency (%)
1207500 1
0.1%
1140750 1
0.1%
1138050 1
0.1%
1048500 1
0.1%
1038100 2
0.3%
1006950 2
0.3%
1006600 1
0.1%
998200 2
0.3%
997850 1
0.1%
982450 1
0.1%

Discounts
Real number (ℝ)

High correlation  Zeros 

Distinct515
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13150.355
Minimum0
Maximum149677.5
Zeros53
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-19T13:05:50.495561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1800.32
median2585.25
Q315956.344
95-th percentile62832
Maximum149677.5
Range149677.5
Interquartile range (IQR)15156.024

Descriptive statistics

Standard deviation22962.929
Coefficient of variation (CV)1.7461832
Kurtosis7.9057124
Mean13150.355
Median Absolute Deviation (MAD)2328.57
Skewness2.6850389
Sum9205248.2
Variance5.272961 × 108
MonotonicityNot monotonic
2024-11-19T13:05:51.018089image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53
 
7.6%
1218.6 3
 
0.4%
5690 3
 
0.4%
20139 3
 
0.4%
4400 2
 
0.3%
870.45 2
 
0.3%
5005.65 2
 
0.3%
3674.4 2
 
0.3%
14906.25 2
 
0.3%
21490 2
 
0.3%
Other values (505) 626
89.4%
ValueCountFrequency (%)
0 53
7.6%
18.41 1
 
0.1%
25.34 1
 
0.1%
44.73 1
 
0.1%
48.15 1
 
0.1%
72.1 1
 
0.1%
91.92 2
 
0.3%
92.82 1
 
0.1%
110.46 1
 
0.1%
112.05 1
 
0.1%
ValueCountFrequency (%)
149677.5 1
0.1%
125820 1
0.1%
119756 2
0.3%
115830 1
0.1%
112927.5 1
0.1%
111375 1
0.1%
109147.5 1
0.1%
106722 1
0.1%
106512 1
0.1%
105367.5 1
0.1%

Sales
Real number (ℝ)

High correlation 

Distinct559
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169609.07
Minimum1655.08
Maximum1159200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-19T13:05:51.566145image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1655.08
5-th percentile4535.65
Q115928
median35540.2
Q3261077.5
95-th percentile683574.75
Maximum1159200
Range1157544.9
Interquartile range (IQR)245149.5

Descriptive statistics

Standard deviation236726.35
Coefficient of variation (CV)1.3957175
Kurtosis2.1886331
Mean169609.07
Median Absolute Deviation (MAD)28716.7
Skewness1.6962952
Sum1.1872635 × 108
Variance5.6039363 × 1010
MonotonicityNot monotonic
2024-11-19T13:05:52.143665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20794.8 2
 
0.3%
334302.5 2
 
0.3%
136560 2
 
0.3%
26945.6 2
 
0.3%
9662.4 2
 
0.3%
21801.6 2
 
0.3%
303688 2
 
0.3%
731472 2
 
0.3%
21700.8 2
 
0.3%
7707.35 2
 
0.3%
Other values (549) 680
97.1%
ValueCountFrequency (%)
1655.08 1
0.1%
1685.6 2
0.3%
1730.54 1
0.1%
1763.86 1
0.1%
1822.59 1
0.1%
2293.2 2
0.3%
2335.76 1
0.1%
2367.4 1
0.1%
2508.66 1
0.1%
3139.2 1
0.1%
ValueCountFrequency (%)
1159200 1
0.1%
1038082.5 1
0.1%
1035625.5 1
0.1%
1017338 2
0.3%
986811 2
0.3%
978236 2
0.3%
962500 1
0.1%
936138 1
0.1%
922680 1
0.1%
884205 1
0.1%

COGS
Real number (ℝ)

High correlation 

Distinct545
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145475.21
Minimum918
Maximum950625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-19T13:05:52.735061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum918
5-th percentile2449.5
Q17490
median22506.25
Q3245607.5
95-th percentile608643.75
Maximum950625
Range949707
Interquartile range (IQR)238117.5

Descriptive statistics

Standard deviation203865.51
Coefficient of variation (CV)1.4013762
Kurtosis1.608463
Mean145475.21
Median Absolute Deviation (MAD)19576.25
Skewness1.5490476
Sum1.0183265 × 108
Variance4.1561145 × 1010
MonotonicityNot monotonic
2024-11-19T13:05:53.280180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17430 4
 
0.6%
8655 3
 
0.4%
24700 3
 
0.4%
1101 3
 
0.4%
15140 3
 
0.4%
615000 3
 
0.4%
79560 3
 
0.4%
335640 2
 
0.3%
12490 2
 
0.3%
204720 2
 
0.3%
Other values (535) 672
96.0%
ValueCountFrequency (%)
918 1
 
0.1%
1101 3
0.4%
1158 2
0.3%
1230 2
0.3%
1285 1
 
0.1%
1315 2
0.3%
1400 2
0.3%
1416 2
0.3%
1465 1
 
0.1%
1500 1
 
0.1%
ValueCountFrequency (%)
950625 1
0.1%
948375 1
0.1%
897000 1
0.1%
873750 1
0.1%
771160 2
0.3%
748250 1
0.1%
748020 2
0.3%
747760 1
0.1%
741520 2
0.3%
741260 1
0.1%

Profit
Real number (ℝ)

High correlation 

Distinct557
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24133.86
Minimum-40617.5
Maximum262200
Zeros5
Zeros (%)0.7%
Negative58
Negative (%)8.3%
Memory size5.6 KiB
2024-11-19T13:05:53.797710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-40617.5
5-th percentile-7849.25
Q12805.96
median9242.2
Q322662
95-th percentile117138.1
Maximum262200
Range302817.5
Interquartile range (IQR)19856.04

Descriptive statistics

Standard deviation42760.627
Coefficient of variation (CV)1.7718105
Kurtosis8.6786162
Mean24133.86
Median Absolute Deviation (MAD)7383.075
Skewness2.7121513
Sum16893702
Variance1.8284712 × 109
MonotonicityNot monotonic
2024-11-19T13:05:54.105839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.7%
10768.8 2
 
0.3%
6822.5 2
 
0.3%
7829.35 2
 
0.3%
11635.6 2
 
0.3%
2952.4 2
 
0.3%
6661.6 2
 
0.3%
165452 2
 
0.3%
47328 2
 
0.3%
-17481.25 2
 
0.3%
Other values (547) 677
96.7%
ValueCountFrequency (%)
-40617.5 1
0.1%
-38046.25 1
0.1%
-35550 1
0.1%
-35262.5 1
0.1%
-33522.5 1
0.1%
-27693.75 1
0.1%
-25841.25 1
0.1%
-24160 1
0.1%
-23870 1
0.1%
-21560 2
0.3%
ValueCountFrequency (%)
262200 1
0.1%
247500 1
0.1%
246178 2
0.3%
238791 2
0.3%
236716 2
0.3%
188378 1
0.1%
186407.5 2
0.3%
165452 2
0.3%
161020 1
0.1%
155250 1
0.1%

Date
Date

Distinct16
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Minimum2013-09-01 00:00:00
Maximum2014-12-01 00:00:00
2024-11-19T13:05:54.376838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:54.630287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)

Month Number
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-19T13:05:54.875699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q15.75
median9
Q310.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.3773206
Coefficient of variation (CV)0.42750894
Kurtosis-0.87915982
Mean7.9
Median Absolute Deviation (MAD)2.5
Skewness-0.57829215
Sum5530
Variance11.406295
MonotonicityNot monotonic
2024-11-19T13:05:55.124198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 140
20.0%
12 105
15.0%
6 70
10.0%
9 70
10.0%
11 70
10.0%
1 35
 
5.0%
3 35
 
5.0%
7 35
 
5.0%
8 35
 
5.0%
2 35
 
5.0%
Other values (2) 70
10.0%
ValueCountFrequency (%)
1 35
 
5.0%
2 35
 
5.0%
3 35
 
5.0%
4 35
 
5.0%
5 35
 
5.0%
6 70
10.0%
7 35
 
5.0%
8 35
 
5.0%
9 70
10.0%
10 140
20.0%
ValueCountFrequency (%)
12 105
15.0%
11 70
10.0%
10 140
20.0%
9 70
10.0%
8 35
 
5.0%
7 35
 
5.0%
6 70
10.0%
5 35
 
5.0%
4 35
 
5.0%
3 35
 
5.0%

Month Name
Categorical

High correlation 

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
October
140 
December
105 
June
70 
September
70 
November
70 
Other values (7)
245 

Length

Max length9
Median length8
Mean length6.6
Min length3

Characters and Unicode

Total characters4620
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowJanuary
3rd rowJune
4th rowJune
5th rowJune

Common Values

ValueCountFrequency (%)
October 140
20.0%
December 105
15.0%
June 70
10.0%
September 70
10.0%
November 70
10.0%
January 35
 
5.0%
March 35
 
5.0%
July 35
 
5.0%
August 35
 
5.0%
February 35
 
5.0%
Other values (2) 70
10.0%

Length

2024-11-19T13:05:55.392973image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
october 140
20.0%
december 105
15.0%
june 70
10.0%
september 70
10.0%
november 70
10.0%
january 35
 
5.0%
march 35
 
5.0%
july 35
 
5.0%
august 35
 
5.0%
february 35
 
5.0%
Other values (2) 70
10.0%

Most occurring characters

ValueCountFrequency (%)
e 910
19.7%
r 560
12.1%
b 420
 
9.1%
c 280
 
6.1%
t 245
 
5.3%
m 245
 
5.3%
u 245
 
5.3%
o 210
 
4.5%
a 175
 
3.8%
O 140
 
3.0%
Other values (16) 1190
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 910
19.7%
r 560
12.1%
b 420
 
9.1%
c 280
 
6.1%
t 245
 
5.3%
m 245
 
5.3%
u 245
 
5.3%
o 210
 
4.5%
a 175
 
3.8%
O 140
 
3.0%
Other values (16) 1190
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 910
19.7%
r 560
12.1%
b 420
 
9.1%
c 280
 
6.1%
t 245
 
5.3%
m 245
 
5.3%
u 245
 
5.3%
o 210
 
4.5%
a 175
 
3.8%
O 140
 
3.0%
Other values (16) 1190
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 910
19.7%
r 560
12.1%
b 420
 
9.1%
c 280
 
6.1%
t 245
 
5.3%
m 245
 
5.3%
u 245
 
5.3%
o 210
 
4.5%
a 175
 
3.8%
O 140
 
3.0%
Other values (16) 1190
25.8%

Year
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2014
525 
2013
175 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2800
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2014
5th row2014

Common Values

ValueCountFrequency (%)
2014 525
75.0%
2013 175
 
25.0%

Length

2024-11-19T13:05:55.646194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T13:05:56.022593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2014 525
75.0%
2013 175
 
25.0%

Most occurring characters

ValueCountFrequency (%)
2 700
25.0%
0 700
25.0%
1 700
25.0%
4 525
18.8%
3 175
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 700
25.0%
0 700
25.0%
1 700
25.0%
4 525
18.8%
3 175
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 700
25.0%
0 700
25.0%
1 700
25.0%
4 525
18.8%
3 175
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 700
25.0%
0 700
25.0%
1 700
25.0%
4 525
18.8%
3 175
 
6.2%

Interactions

2024-11-19T13:05:42.383636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:22.919426image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:26.200166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:28.384634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:30.422515image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:32.710558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:34.825390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:37.050914image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:40.298924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:42.579859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:23.255134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:26.496444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:28.591542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:30.623976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:32.931798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:35.044285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:37.366181image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:40.498812image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:42.803750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:23.573121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:26.710730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:28.816329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:30.905026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:33.161081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:35.266718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:37.697913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:40.730762image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:43.045200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:23.923809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:26.951979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:29.046285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:31.323387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:33.412234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:35.495854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:38.037625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:40.996553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:43.267986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:24.281280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:27.192165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:29.275306image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:31.561037image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:33.659659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:35.765227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:38.345965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:41.232236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:43.485919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:24.823092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:27.436746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:29.513757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:31.815248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:33.899964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:35.999753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:39.009286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:41.462276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:43.713003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:25.165768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:27.660316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:29.764070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:32.048241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:34.144989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:36.226850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:39.364842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:41.697651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:43.933144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:25.485240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:27.898876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:29.968366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:32.253402image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:34.364987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:36.432928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:39.695694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:41.936116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:44.177283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:25.842308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:28.141943image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:30.193441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:32.492122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:34.607793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:36.755507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:40.061150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T13:05:42.162509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-19T13:05:56.232592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
SalesCOGSCountryDiscount BandDiscountsGross SalesManufacturing PriceMonth NameMonth NumberProductProfitSale PriceSegmentUnits SoldYear
Sales1.0000.9710.0660.1230.7730.9990.0300.068-0.0340.0000.5810.8690.3650.3760.155
COGS0.9711.0000.0620.1100.7770.9730.0320.075-0.0230.0000.4740.8520.3900.3650.111
Country0.0660.0621.0000.0000.0000.0350.0000.0000.0000.0000.0690.0000.0000.1000.000
Discount Band0.1230.1100.0001.0000.1700.1190.0330.0900.0810.0000.2160.0830.0930.1340.000
Discounts0.7730.7770.0000.1701.0000.7910.0570.0860.0080.0000.3500.7070.2560.2720.088
Gross Sales0.9990.9730.0350.1190.7911.0000.0320.067-0.0300.0000.5760.8700.3770.3770.087
Manufacturing Price0.0300.0320.0000.0330.0570.0321.0000.0000.0040.9980.0360.0560.000-0.0230.000
Month Name0.0680.0750.0000.0900.0860.0670.0001.0000.9990.0000.0000.0000.0000.1500.523
Month Number-0.034-0.0230.0000.0810.008-0.0300.0040.9991.0000.000-0.0440.0000.000-0.0660.517
Product0.0000.0000.0000.0000.0000.0000.9980.0000.0001.0000.0000.0000.0000.0000.000
Profit0.5810.4740.0690.2160.3500.5760.0360.000-0.0440.0001.0000.5250.3740.2950.000
Sale Price0.8690.8520.0000.0830.7070.8700.0560.0000.0000.0000.5251.0000.848-0.0590.000
Segment0.3650.3900.0000.0930.2560.3770.0000.0000.0000.0000.3740.8481.0000.0850.000
Units Sold0.3760.3650.1000.1340.2720.377-0.0230.150-0.0660.0000.295-0.0590.0851.0000.095
Year0.1550.1110.0000.0000.0880.0870.0000.5230.5170.0000.0000.0000.0000.0951.000

Missing values

2024-11-19T13:05:44.502867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-19T13:05:45.036763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SegmentCountryProductDiscount BandUnits SoldManufacturing PriceSale PriceGross SalesDiscountsSalesCOGSProfitDateMonth NumberMonth NameYear
0GovernmentCanadaCarreteraNaN1618.532032370.00.032370.016185.016185.02014-01-011January2014
1GovernmentGermanyCarreteraNaN1321.032026420.00.026420.013210.013210.02014-01-011January2014
2MidmarketFranceCarreteraNaN2178.031532670.00.032670.021780.010890.02014-06-016June2014
3MidmarketGermanyCarreteraNaN888.031513320.00.013320.08880.04440.02014-06-016June2014
4MidmarketMexicoCarreteraNaN2470.031537050.00.037050.024700.012350.02014-06-016June2014
5GovernmentGermanyCarreteraNaN1513.03350529550.00.0529550.0393380.0136170.02014-12-0112December2014
6MidmarketGermanyMontanaNaN921.051513815.00.013815.09210.04605.02014-03-013March2014
7Channel PartnersCanadaMontanaNaN2518.051230216.00.030216.07554.022662.02014-06-016June2014
8GovernmentFranceMontanaNaN1899.052037980.00.037980.018990.018990.02014-06-016June2014
9Channel PartnersGermanyMontanaNaN1545.051218540.00.018540.04635.013905.02014-06-016June2014
SegmentCountryProductDiscount BandUnits SoldManufacturing PriceSale PriceGross SalesDiscountsSalesCOGSProfitDateMonth NumberMonth NameYear
690GovernmentUnited States of AmericaVTTHigh267.0250205340.0801.004539.002670.01869.002013-10-0110October2013
691MidmarketGermanyVTTHigh1175.02501517625.02643.7514981.2511750.03231.252014-10-0110October2014
692EnterpriseCanadaVTTHigh2954.0250125369250.055387.50313862.50354480.0-40617.502013-11-0111November2013
693EnterpriseGermanyVTTHigh552.025012569000.010350.0058650.0066240.0-7590.002014-11-0111November2014
694GovernmentFranceVTTHigh293.0250205860.0879.004981.002930.02051.002014-12-0112December2014
695Small BusinessFranceAmarillaHigh2475.0260300742500.0111375.00631125.00618750.012375.002014-03-013March2014
696Small BusinessMexicoAmarillaHigh546.0260300163800.024570.00139230.00136500.02730.002014-10-0110October2014
697GovernmentMexicoMontanaHigh1368.0579576.01436.408139.606840.01299.602014-02-012February2014
698GovernmentCanadaPaseoHigh723.01075061.0759.154301.853615.0686.852014-04-014April2014
699Channel PartnersUnited States of AmericaVTTHigh1806.02501221672.03250.8018421.205418.013003.202014-05-015May2014